Classification of Liquid Ingress in GFRP Honeycomb Based on One-Dimension Sequential Model Using THz-TDS

Abstract

Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid ingress. Here, we report a fast and automatic classification approach for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures through terahertz time-domain spectroscopy (THz-TDS). We propose an improved one-dimensional convolutional neural network (1D-CNN) model, and compared it with long short-term memory (LSTM) and ordinary 1D-CNN models, which are classification networks based on one dimension sequenced signals. The automated liquid classification results show that the LSTM model has the best performance for the time-domain signals, while the improved 1D-CNN model performed best for the frequency-domain signals.

Water ingress detection in an aluminum honeycomb sandwich structure using laser ultrasonics based on mode conversion behaviors

“…The capacitive imaging approach 13 can also detect water ingress in honeycomb sandwich structures; nevertheless, the imaging results are not accurate enough due to the dependence on the electrical conductivity of the sample. Microwave testing 14 and terahertz time-domain spectroscopy (THz-TDS) 15 are both novel NDI techniques for water ingress detection. However, the main limitations of these techniques are the weak penetration in metals and the high equipment cost.…”